maestroserver/analytics-maestro repository overview

⁠Maestro Server

Maestro Server is an open source software platform for management and discovery servers, apps and system for Hybrid IT. Can manage small and large environments, be able to visualize the latest multi-cloud environment state.

⁠Demo

To test out the demo, Demo Online⁠

⁠Documentation

• UserGuide⁠
• API Contract⁠

⁠Maestro Server - Analytics API

Draw archtectures maps using NetworkX Graph and SVG write.

Core API, organized by modules:

• Create grids
• Create bussiness graph
• Create network graph
• Create infra graph
• Drawing
• SVGs

⁠TechStack

• Python 3.5
• Flask 3.5

⁠Service dependencies

• Maestro Server
• Maestro Analytics Front

⁠Setup

⁠Installation by docker

docker run -p 5020
    -e "MAESTRO_DATA_URI=http://localhost:5010"
    -e "CELERY_BROKER_URL=amqp://rabbitmq:5672"
    -e 'MAESTRO_MONGO_URI=localhost'
    maestroserver/analytics-maestro

docker run
    -e "MAESTRO_DATA_URI=http://localhost:5010"
    -e "MAESTRO_ANALYTICS_FRONT_URI=http://localhost:9999"
    -e "CELERY_BROKER_URL=amqp://rabbitmq:5672"
    maestroserver/analytics-maestro-celery

Or by docker-compose

version: '2'

services:
    analytics:
        image: maestroserver/analytics-maestro
        ports:
        - "5020:5020"
        environment:
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "MAESTRO_DATA_URI=http://data:5010"

    analytics_worker:
        image: maestroserver/analytics-maestro-celery
        environment:
        - "MAESTRO_DATA_URI=http://data:5010"
        - "MAESTRO_ANALYTICS_FRONT_URI=http://analytics_front:9999"
        - "CELERY_BROKER_URL=amqp://rabbitmq:5672"
        - "CELERYD_MAX_TASKS_PER_CHILD=2"

⁠Dev Env

Run python and celery.

cd devtools/

docker-compose up -d

Configure rabbitmq service in .env file

CELERY_BROKER_URL="amqp://localhost:5672"
CELERYD_TASK_TIME_LIMIT=30

Install pip dependences

pip install -r requeriments.txt

Run server

python -m flask run.py

or

FLASK_APP=run.py FLASK_DEBUG=1 flask run

or 

npm run server

Run workers

celery -A app.celery worker -E -Q discovery --hostname=discovery@%h --loglevel=info

or 

npm run celery

Run all tests

python -m unittest discover

Create doc

npm install
apidoc -i app/controller/ -o docs/

or 

npm run docs

⁠Important notes

• Controller used only graph to start all tasks:

• The drawer process is compound by:

  • entry: First task, figure out all entry applications accordingly system endpoint parameters, our any direct application if avalaible.

  • graphlookup: Request for Data App a aggregate query using MongoDB $graphLookup.

  • network bussiness: Construct Grid Map, and send to enrichment and info bussines.

  • enrichment: Request for Data App all servers used on grid.

  • info bussiness: Calculate histogram, counts, density and connections.

  • network client: Request for Data App all clients used in grid.

  • draw bussiness: Create svgs based of grid.

  • notification: Send updates for Data App.

  • send front app: Send svgs to Analytics Front app.

• Each step have unique task.

  • Config is managed by env variables, need to be, because in production env like k8s is easier to manager the pods.

  • Repository it's pymongo objects.

⁠Env variables

⁠Contribute

Are you interested in developing Maestro Server, creating new features or extending them?

We created a set of documentation, explaining how to set up your development environment, coding styles, standards, learn about the architecture and more. Welcome to the team and contribute with us.

See our developer guide⁠